Abstract
Fungal pathogens pose significant threats to plant health by secreting effectors that manipulate plant-host defences. However, identifying effector proteins remains challenging, in part because they lack common sequence motifs. Here, we introduce Fungtion (Fungal effector prediction), a toolkit leveraging a hybrid framework to accurately predict and visualize fungal effectors. By combining global patterns learned from pretrained protein language models with refined information from known effectors, Fungtion achieves state-of-the-art prediction performance. Additionally, the interactive visualizations we have developed enable researchers to explore both sequence- and high-level relationships between the predicted and known effectors, facilitating effector function discovery, annotation, and hypothesis formulation regarding plant-pathogen interactions. We anticipate Fungtion to be a valuable resource for biologists seeking deeper insights into fungal effector functions and for computational biologists aiming to develop future methodologies for fungal effector prediction: https://step3.erc.monash.edu/Fungtion/.
Original language | English |
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Pages (from-to) | 168613 |
Number of pages | 9 |
Journal | Journal of Molecular Biology |
Volume | 436 |
Issue number | 17 |
Early online date | 20 May 2024 |
DOIs | |
Publication status | Published - 3 Sept 2024 |
Data Availability Statement
Data will be made available on request.Funding
J.W. is a recipient of Marie Skłodowska-Curie Postdoctoral Fellowship, EMBL Interdisciplinary Postdoctoral (EIPOD) Fellowship and EMBO Non-Stipendiary Fellowship (EMBO ALTF 400-2022), and a Junior Research Fellow at Wolfson College, the University of Cambridge, UK. C.J.S. is an Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) Fellow (DE230100700).